There are in fact very many methods for evaluating statistical models (Claeskens and Hjort 2008, Wagenmakers and Waldorp 2006). In first instance, the methods occasion the comparison of statistical models, but very often they are used for selecting one model over the others. In what follows we only review prominent techniques that have led to philosophical debate: Akaike's information criterion, the Bayesian information criterion, and furthermore the computation of marginal likelihoods and posterior model probabilities, both associated with Bayesian model selection. We leave aside methods that use cross-validation as they have, unduly, not received as much attention in the philosophical literature.