Research on model (or procedure) selection/combining in statistical learning has focused on selecting/combining models globally. In many applications, especially for high-dimensional or complex data, however, the relative performance of the candidate procedures typically depends on the location, and the globally best procedure can often be much improved when selection/combination of procedures is allowed to depend on location. We develop methods for localized model selection/combining and derive their theoretical properties.