Performance issue in Series with MultiIndex
#### Code Sample
```python
In [81]: sd_series = pd.Series(range(1000), index=pd.MultiIndex.from_product([range(100), range(10)]))

In [82]: def f_loc():
    ...:     for l1 in sd_series.index.levels[0]:
    ...:         _ = sd_series.loc[l1]
    ...:         

In [83]: def f_iloc():
    ...:     for i, l1 in enumerate(sd_series.index.levels[0]):
    ...:         _ = sd_series.iloc[10*i:10*(i+1)]
    ...: 

In [84]: def f_pd_groupby():
    ...:     for l1, group in sd_series.groupby(level=0):
    ...:         _ = group
    ...:         

In [85]: def f_groupby():
    ...:     for l1, group in groupby(sd_series.items(), lambda it: it[0][0]):
    ...:         _ = group
    ...:         
    ...:         

In [86]: %timeit f_loc()
55.7 ms ± 1.78 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [87]: %timeit f_iloc()
18.8 ms ± 665 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [88]: %timeit f_pd_groupby()
17.5 ms ± 230 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [89]: %timeit f_groupby()
374 µs ± 6.77 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
#### Problem description

There seems to be a significant performance issue when extracting data from a `Series` with a `MultiIndex` so that it is actually faster (by a factor 100) to use `itertools.groupby` than either `loc`, `iloc`, or `pd.groupby`.
I've found some issues with `MultiIndex` in the past with `get_loc` but that has been fixed. Apologies if I missed something else.

#### Output of ``pd.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.22.0
pytest: None
pip: 9.0.1
setuptools: 36.5.0
Cython: None
numpy: 1.14.0
scipy: None
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
