diff --git a/pandas/io/excel/_base.py b/pandas/io/excel/_base.py
index 6eb1b9e950dfd..8574c9ad1d425 100644
--- a/pandas/io/excel/_base.py
+++ b/pandas/io/excel/_base.py
@@ -133,8 +133,24 @@
     + fill("', '".join(sorted(_NA_VALUES)), 70, subsequent_indent="    ")
     + """'.
 keep_default_na : bool, default True
-    If na_values are specified and keep_default_na is False the default NaN
-    values are overridden, otherwise they're appended to.
+    Whether or not to include the default NaN values when parsing the data.
+    Depending on whether `na_values` is passed in, the behavior is as follows:
+
+    * If `keep_default_na` is True, and `na_values` are specified, `na_values`
+      is appended to the default NaN values used for parsing.
+    * If `keep_default_na` is True, and `na_values` are not specified, only
+      the default NaN values are used for parsing.
+    * If `keep_default_na` is False, and `na_values` are specified, only
+      the NaN values specified `na_values` are used for parsing.
+    * If `keep_default_na` is False, and `na_values` are not specified, no
+      strings will be parsed as NaN.
+
+    Note that if `na_filter` is passed in as False, the `keep_default_na` and
+    `na_values` parameters will be ignored.
+na_filter : bool, default True
+    Detect missing value markers (empty strings and the value of na_values). In
+    data without any NAs, passing na_filter=False can improve the performance
+    of reading a large file.
 verbose : bool, default False
     Indicate number of NA values placed in non-numeric columns.
 parse_dates : bool, list-like, or dict, default False
diff --git a/pandas/tests/io/excel/test_readers.py b/pandas/tests/io/excel/test_readers.py
index 5326f2df68972..1d3653f685e1e 100644
--- a/pandas/tests/io/excel/test_readers.py
+++ b/pandas/tests/io/excel/test_readers.py
@@ -870,6 +870,27 @@ def test_excel_passes_na(self, read_ext):
         )
         tm.assert_frame_equal(parsed, expected)
 
+    @pytest.mark.parametrize("na_filter", [None, True, False])
+    def test_excel_passes_na_filter(self, read_ext, na_filter):
+        # gh-25453
+        kwargs = {}
+
+        if na_filter is not None:
+            kwargs["na_filter"] = na_filter
+
+        with pd.ExcelFile("test5" + read_ext) as excel:
+            parsed = pd.read_excel(
+                excel, "Sheet1", keep_default_na=True, na_values=["apple"], **kwargs
+            )
+
+        if na_filter is False:
+            expected = [["1.#QNAN"], [1], ["nan"], ["apple"], ["rabbit"]]
+        else:
+            expected = [[np.nan], [1], [np.nan], [np.nan], ["rabbit"]]
+
+        expected = DataFrame(expected, columns=["Test"])
+        tm.assert_frame_equal(parsed, expected)
+
     @pytest.mark.parametrize("arg", ["sheet", "sheetname", "parse_cols"])
     def test_unexpected_kwargs_raises(self, read_ext, arg):
         # gh-17964