pd.Series.equals() fails for Series containing iterable objects
#### Code Sample, a copy-pastable example if possible

```python
pd.Series(
    [np.array([1, 2]), np.array([1, 2])]
).equals(pd.Series(
    [np.array([1, 2]), np.array([1, 2])]
))

# Evaluates to False
```
#### Problem description

For objects that overload equality with elementwise equality (this includes `np.array` and `pd.Series` objects), `pd.Series.equals()` catches

```python
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```

and returns `False`, behavior inherited from `np.equal` applied to object arrays. While you can argue this is a numpy bug, in numpy equality is purposefully strict for `dtype=='object'`. In pandas, it is a common enough procedure to tabulate arrays and lists under a single column (which may contain a named (time)series, image array, or an unpacked NLP document), and this behavior of equality is unintuitive.

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-1048-aws
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.22.0
pytest: 3.2.2
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.13.3
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 5.3.0
sphinx: 1.5.4
patsy: None
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
