In an era where data-based decisions are paramount, predictive modeling has become an increasingly important tool in driving innovation. Model selection criteria, quantitative measures that allow for the rank-ordering of a set of considered models, are often used to choose the best predictive model among a defined collection. Our work develops a new class of model selection criteria that improve among existing criteria in their selection of predictive models that best minimize prediction error.