[MRG] Sparse One vs. Rest

This is a PR for the other parts in PR #2458 concerning sparse output support for the ovr classier. This branch was made off of the sprs-lbl-bin branch from PR #3203 and will be rebased to remove all the sparse label binarizer additions from the diff once the pull request is merged.

Benchmark Experiments using PR #2828 for data generation:
- [x] ```fit_ovr```: ```sparse_output=True``` VS. ```sparse_output=sp.issparse(y)```
- [x] ```fit_ovr``` With input as CSR matrix: Cast to CSC before column extraction VS. Leave as CSR
- [x] Memory profiling for sparse vs. dense target data
- [x] Test the changes to label.py
- [x] Test probability and desicion functions after fitting on sparse target data
- [x] Memory benchmark with ```njobs != 1```
Please add a comment (or a sentence in the docs) stating that `n_jobs > 1` can be slower than `n_jobs == 1` when the individual binary classifiers are very fast to fit (as in the case in @arjoly's benchmark.)

You can add a comment in the source referencing [this joblib issue](https://github.com/joblib/joblib/issues/112).