  Used to estimate the risk of an estimator or to perform model selection,
cross-validation is a widespread strategy because of its simplicity and its
apparent universality. Many results exist on the model selection performances
of cross-validation procedures. This survey intends to relate these results to
the most recent advances of model selection theory, with a particular emphasis
on distinguishing empirical statements from rigorous theoretical results. As a
conclusion, guidelines are provided for choosing the best cross-validation
procedure according to the particular features of the problem in hand.
