diff --git a/pytorch_lightning/accelerators/accelerator.py b/pytorch_lightning/accelerators/accelerator.py
index 0600aca92e29f..2ba97d7d9a725 100644
--- a/pytorch_lightning/accelerators/accelerator.py
+++ b/pytorch_lightning/accelerators/accelerator.py
@@ -383,12 +383,18 @@ def barrier(self, name: Optional[str] = None) -> None:
         self.training_type_plugin.barrier(name=name)
 
     def broadcast(self, obj: object, src: int = 0) -> object:
-        """Broadcasts an object to all processes"""
+        """Broadcasts an object to all processes, such that the src object is broadcast to all other ranks if needed.
+
+        Args:
+            obj: Object to broadcast to all process, usually a tensor or collection of tensors.
+            src: The source rank of which the object will be broadcast from
+        """
         return self.training_type_plugin.broadcast(obj, src)
 
     def all_gather(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False):
         """
-        Function to gather a tensor from several distributed processes
+        Function to gather a tensor from several distributed processes.
+
         Args:
             tensor: tensor of shape (batch, ...)
             group: the process group to gather results from. Defaults to all processes (world)
@@ -409,8 +415,7 @@ def process_dataloader(self, dataloader: Union[Iterable, DataLoader]) -> Union[I
     @property
     def results(self) -> Any:
         """
-        The results of the last training/testing run will be cached here.
+        The results of the last training/testing run will be cached within the training type plugin.
         In distributed training, we make sure to transfer the results to the appropriate master process.
         """
-        # TODO: improve these docs
         return self.training_type_plugin.results