We provide a brief overview of both Bayes and classical model selection. We argue tentatively that model selection has at least two major goals, that of finding the correct model or predicting well, and that in general both these goals may not be achieved in an optimum manner by a single model selection rule. We discuss, briefly but critically, through a study of well-known model selection rules like AIC, BIC, DIC and Lasso, how these different goals are pursued in each paradigm. We introduce some new definitions of consistency, results and conjectures about consistency in high dimensional model selection problems. Finally we discuss some new or recent results in Full Bayes and Empirical Bayes multiple testing, and cross-validation. We show that when the number of parameters tends to infinity at a smaller rate than sample size, then it is best from the point of view of consistency to use most of the data for inference and only a negligible proportion to make an improper prior proper.