diff --git a/docs/source/extensions/logging.rst b/docs/source/extensions/logging.rst
index ed837553c85f7..8d782a9c478c9 100644
--- a/docs/source/extensions/logging.rst
+++ b/docs/source/extensions/logging.rst
@@ -90,7 +90,7 @@ The :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method has a
 .. note::
 
     -   Setting ``on_epoch=True`` will cache all your logged values during the full training epoch and perform a
-        reduction `on_epoch_end`. We recommend using the :doc:`metrics <../extensions/metrics>` API when working with custom reduction.
+        reduction in ``on_train_epoch_end``. We recommend using the :doc:`metrics <../extensions/metrics>` API when working with custom reduction.
 
     -   Setting both ``on_step=True`` and ``on_epoch=True`` will create two keys per metric you log with
         suffix ``_step`` and ``_epoch``, respectively. You can refer to these keys e.g. in the `monitor`
diff --git a/pytorch_lightning/callbacks/base.py b/pytorch_lightning/callbacks/base.py
index 0ba1fd4ff7785..3bae1204c47e1 100644
--- a/pytorch_lightning/callbacks/base.py
+++ b/pytorch_lightning/callbacks/base.py
@@ -102,11 +102,11 @@ def on_test_epoch_end(self, trainer, pl_module: LightningModule) -> None:
         pass
 
     def on_epoch_start(self, trainer, pl_module: LightningModule) -> None:
-        """Called when the epoch begins."""
+        """Called when either of train/val/test epoch begins."""
         pass
 
     def on_epoch_end(self, trainer, pl_module: LightningModule) -> None:
-        """Called when the epoch ends."""
+        """Called when either of train/val/test epoch ends."""
         pass
 
     def on_batch_start(self, trainer, pl_module: LightningModule) -> None:
diff --git a/pytorch_lightning/callbacks/gradient_accumulation_scheduler.py b/pytorch_lightning/callbacks/gradient_accumulation_scheduler.py
index 0af7d61bf5dec..b1885087f4da0 100644
--- a/pytorch_lightning/callbacks/gradient_accumulation_scheduler.py
+++ b/pytorch_lightning/callbacks/gradient_accumulation_scheduler.py
@@ -74,7 +74,7 @@ def __init__(self, scheduling: Dict[int, int]):
     def going_to_accumulate_grad_batches(self):
         return any([v > 1 for v in self.scheduling.values()])
 
-    def on_epoch_start(self, trainer, pl_module):
+    def on_train_epoch_start(self, trainer, pl_module):
         epoch = trainer.current_epoch
         for i in reversed(range(len(self.epochs))):
             if epoch >= self.epochs[i]:
diff --git a/pytorch_lightning/callbacks/progress.py b/pytorch_lightning/callbacks/progress.py
index 74e57e2b5642e..365ed4d1b90ac 100644
--- a/pytorch_lightning/callbacks/progress.py
+++ b/pytorch_lightning/callbacks/progress.py
@@ -201,7 +201,7 @@ def on_init_end(self, trainer):
     def on_train_start(self, trainer, pl_module):
         self._train_batch_idx = trainer.batch_idx
 
-    def on_epoch_start(self, trainer, pl_module):
+    def on_train_epoch_start(self, trainer, pl_module):
         self._train_batch_idx = 0
 
     def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
@@ -393,8 +393,8 @@ def on_train_start(self, trainer, pl_module):
         super().on_train_start(trainer, pl_module)
         self.main_progress_bar = self.init_train_tqdm()
 
-    def on_epoch_start(self, trainer, pl_module):
-        super().on_epoch_start(trainer, pl_module)
+    def on_train_epoch_start(self, trainer, pl_module):
+        super().on_train_epoch_start(trainer, pl_module)
         total_train_batches = self.total_train_batches
         total_val_batches = self.total_val_batches
         if total_train_batches != float('inf'):
diff --git a/pytorch_lightning/core/hooks.py b/pytorch_lightning/core/hooks.py
index 1399d1b3c66ba..fb2ca7edc6a2b 100644
--- a/pytorch_lightning/core/hooks.py
+++ b/pytorch_lightning/core/hooks.py
@@ -224,13 +224,13 @@ def on_predict_model_eval(self) -> None:
 
     def on_epoch_start(self) -> None:
         """
-        Called in the training loop at the very beginning of the epoch.
+        Called when either of train/val/test epoch begins.
         """
         # do something when the epoch starts
 
     def on_epoch_end(self) -> None:
         """
-        Called in the training loop at the very end of the epoch.
+        Called when either of train/val/test epoch ends.
         """
         # do something when the epoch ends
 
diff --git a/pytorch_lightning/trainer/callback_hook.py b/pytorch_lightning/trainer/callback_hook.py
index 5aa9f1a44276b..c8b5844c3e000 100644
--- a/pytorch_lightning/trainer/callback_hook.py
+++ b/pytorch_lightning/trainer/callback_hook.py
@@ -15,7 +15,7 @@
 from abc import ABC
 from copy import deepcopy
 from inspect import signature
-from typing import Any, Callable, Dict, List, Type, Optional
+from typing import Any, Callable, Dict, List, Optional, Type
 
 from pytorch_lightning.callbacks import Callback
 from pytorch_lightning.core.lightning import LightningModule
@@ -105,12 +105,12 @@ def on_test_epoch_end(self):
             callback.on_test_epoch_end(self, self.lightning_module)
 
     def on_epoch_start(self):
-        """Called when the epoch begins."""
+        """Called when either of train/val/test epoch begins."""
         for callback in self.callbacks:
             callback.on_epoch_start(self, self.lightning_module)
 
     def on_epoch_end(self):
-        """Called when the epoch ends."""
+        """Called when either of train/val/test epoch ends."""
         for callback in self.callbacks:
             callback.on_epoch_end(self, self.lightning_module)