[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
Other suggestion: add one option to avoid refitting and do model averaging on all the folds (probably a refit=True argument to the constructor).

The idea is the following: for each fold (and each class, in a multiple class setting), you take the coefficients corresponding to smallest out of bag error (ie a different index per fold), you average these, and you keep them as your final coefs_. This has two benefits: it avoids the final recomputation, and it has a slight bootstrapping effect, that makes things a bit more stable (in particular it makes the choice of regularization parameter a bit more robust).