Let $\mathcal{F}$ be a class of measurable functions $f:S\mapsto [0,1]$ defined on a probability space $(S,\mathcal{A},P)$. Given a sample (X_1,...,X_n) of i.i.d. random variables taking values in S with common distribution P, let P_n denote the empirical measure based on (X_1,...,X_n). We study an empirical risk minimization problem $P_nf\to \min$, $f\in \mathcal{F}$. Given a solution $\hat{f}_n$ of this problem, the goal is to obtain very general upper bounds on its excess risk \[\mathcal{E}_P(\hat{f}_n):=P\hat{f}_n-\inf_{f\in \mathcal{F}}Pf,\] expressed in terms of relevant geometric parameters of the class $\mathcal{F}$. Using concentration inequalities and other empirical processes tools, we obtain both distribution-dependent and data-dependent upper bounds on the excess risk that are of asymptotically correct order in many examples. The bounds involve localized sup-norms of empirical and Rademacher processes indexed by functions from the class. We use these bounds to develop model selection techniques in abstract risk minimization problems that can be applied to more specialized frameworks of regression and classification.