Title: Data-driven penalties for model selection Abstract: Penalization procedures often suffer from their dependence on multiplying factors, whose optimal values are either unknown or hard to estimate from data. We propose a completely data-driven calibration algorithm for this parameter in the least-squares regression framework, without assuming a particular shape for the penalty. Moreover, dimensionality-based penalties such as Mallows' Cp fail in the heteroscedastic regression framework, so that the shape of the penalty itself has to be estimated. Resampling is used for building penalties robust to heteroscedasticity, without requiring prior information on the noise-level. For instance, V-fold penalization is shown to improve V-fold cross-validation for a fixed computational cost.