diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index 2604b6e0784cf..fa9a5cf12570d 100755
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -132,8 +132,10 @@ class ParserWarning(Warning):
 
     Note: A fast-path exists for iso8601-formatted dates.
 infer_datetime_format : boolean, default False
-    If True and parse_dates is enabled for a column, attempt to infer
-    the datetime format to speed up the processing
+    If True and parse_dates is enabled, pandas will attempt to infer the format
+    of the datetime strings in the columns, and if it can be inferred, switch
+    to a faster method of parsing them. In some cases this can increase the
+    parsing speed by ~5-10x.
 keep_date_col : boolean, default False
     If True and parse_dates specifies combining multiple columns then
     keep the original columns.
diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py
index d92cfef5280fc..f9df0d082f2ff 100644
--- a/pandas/tseries/tools.py
+++ b/pandas/tseries/tools.py
@@ -231,8 +231,10 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
     unit : unit of the arg (D,s,ms,us,ns) denote the unit in epoch
         (e.g. a unix timestamp), which is an integer/float number.
     infer_datetime_format : boolean, default False
-        If no `format` is given, try to infer the format based on the first
-        datetime string. Provides a large speed-up in many cases.
+        If True and no `format` is given, attempt to infer the format of the
+        datetime strings, and if it can be inferred, switch to a faster
+        method of parsing them. In some cases this can increase the parsing
+        speed by ~5-10x.
 
     Returns
     -------
@@ -273,6 +275,19 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
     99   2000-04-09
     Length: 100, dtype: datetime64[ns]
 
+    Infer the format from the first entry
+
+    >>> pd.to_datetime(df.month + '/' +  df.day + '/' + df.year,
+                       infer_datetime_format=True)
+    0    2000-01-01
+    1    2000-01-02
+    ...
+    98   2000-04-08
+    99   2000-04-09
+
+    This gives the same results as omitting the `infer_datetime_format=True`,
+    but is much faster.
+
     Date that does not meet timestamp limitations:
 
     >>> pd.to_datetime('13000101', format='%Y%m%d')