Abstract: Cross-validation is one of the most widely used tools for model quality assessment and comparison. When combined with appropriate notions of stability, cross-validation can be adapted to solve many interesting inference problems. In this talk, I will describe two examples. The first is a variant of cross-validation, called "rolling validation," which can achieve superior model selection accuracy for batch data and is naturally extendable to online problems. The second is the construction of confidence sets in discrete population comparison or model selection problems.