diff --git a/doc/source/user_guide/reshaping.rst b/doc/source/user_guide/reshaping.rst
index 3347f3a2534f4..8c5e98791a9ef 100644
--- a/doc/source/user_guide/reshaping.rst
+++ b/doc/source/user_guide/reshaping.rst
@@ -321,7 +321,7 @@ The missing value can be filled with a specific value with the ``fill_value`` ar
 .. image:: ../_static/reshaping_melt.png
 
 The top-level :func:`~pandas.melt` function and the corresponding :meth:`DataFrame.melt`
-are useful to massage a :class:`DataFrame` into a format where one or more columns
+are useful to reshape a :class:`DataFrame` into a format where one or more columns
 are *identifier variables*, while all other columns, considered *measured
 variables*, are "unpivoted" to the row axis, leaving just two non-identifier
 columns, "variable" and "value". The names of those columns can be customized
diff --git a/pandas/core/reshape/melt.py b/pandas/core/reshape/melt.py
index bfd8e3ccd2f7c..f4cb82816bbcf 100644
--- a/pandas/core/reshape/melt.py
+++ b/pandas/core/reshape/melt.py
@@ -51,9 +51,9 @@ def melt(
     """
     Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
 
-    This function is useful to massage a DataFrame into a format where one
+    This function is useful to reshape a DataFrame into a format where one
     or more columns are identifier variables (`id_vars`), while all other
-    columns, considered measured variables (`value_vars`), are "unpivoted" to
+    columns are considered measured variables (`value_vars`), and are "unpivoted" to
     the row axis, leaving just two non-identifier columns, 'variable' and
     'value'.