diff --git a/doc/source/user_guide/indexing.rst b/doc/source/user_guide/indexing.rst
index b123e381f082a..de8006d2168af 100644
--- a/doc/source/user_guide/indexing.rst
+++ b/doc/source/user_guide/indexing.rst
@@ -120,6 +120,16 @@ indexing pandas objects with ``[]``:
 
     Series, ``series[label]``, scalar value
     DataFrame, ``frame[colname]``, ``Series`` corresponding to colname
+    DataFrame, ``frame[[colname]]``, ``DataFrame`` with columns corresponding to ``[colname]``
+    DataFrame, ``frame[list_of_colnames]``, ``DataFrame`` with columns corresponding to ``list_of_colnames``
+
+The same principle applies to label-based selection with ``.loc``: a
+list-like of labels preserves the corresponding axis (so
+``df.loc[:, ["A"]]`` returns a ``DataFrame``, even when the list contains a
+single label), while a scalar label reduces the axis when labels on that
+axis are unique (so ``df.loc[:, "A"]`` typically returns a ``Series``; if
+``"A"`` is duplicated on the axis, a ``DataFrame`` is returned instead).
+See :ref:`indexing.label` for details.
 
 Here we construct a simple time series data set to use for illustrating the
 indexing functionality:
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index a6b1815c8c257..5bcb8078870bf 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -1379,6 +1379,22 @@ class _LocIndexer(_LocationIndexer):
     viper               4       5
     sidewinder          7       8
 
+    Single column label. Note this returns the column as a Series.
+
+    >>> df.loc[:, "max_speed"]
+    cobra         1
+    viper         4
+    sidewinder    7
+    Name: max_speed, dtype: int64
+
+    List with a single column label. Note this returns a DataFrame.
+
+    >>> df.loc[:, ["max_speed"]]
+                max_speed
+    cobra               1
+    viper               4
+    sidewinder          7
+
     Single label for row and column
 
     >>> df.loc["cobra", "shield"]