[MRG] Logistic Regression CV

Continuation of the work by @fabianp and @GaelVaroquaux 

TODO's

- [x] I think the some of the private functions in logistic.py can be simplified. In particular, it is my intuition that we could squash some of the functions _logistic* and _logistic_*_intercept without sacrificing a significant amount of performance

- [x] sparse matrix support (any help from the people who actually use sparse matrices would be great here)

- [x] implement multinomial logistic or raise a meaningful error for multiclass pointing to OvA, OvO meta-estimators

- [x] More tests for LogisticCV object (and some are failing)

- [x] Document tolerance and stopping criterions
> As it is coefs_paths_ is n_folds X len(Cs) X n_features. Adding another
> dimension would make the API complex IMO. We could have a dictionary
> maybe with keys as the n_th class?

> The same opinion goes for, self.scores_

I think that I agree with your analysis and your conclusions. This is
probably the approach that I would favor.