  The purpose of this paper is to explain the interest and importance of
(approximate) models and model selection in Statistics. Starting from the very
elementary example of histograms we present a general notion of finite
dimensional model for statistical estimation and we explain what type of risk
bounds can be expected from the use of one such model. We then give the
performance of suitable model selection procedures from a family of such
models. We illustrate our point of view by two main examples: the choice of a
partition for designing a histogram from an n-sample and the problem of
variable selection in the context of Gaussian regression.
