Machine learning methods provide increasingly powerful tools for generating predictions about human behavior. However, simply using off the shelf methods to generate predictions potentially misses opportunities to benefit from and contribute to the psychological literature. In this talk I will discuss three ways in which theory and data can interact through machine learning: using theories to pretrain machine learning models; using theories to constrain machine learning models; and using unconstrained machine learning models to critique explanatory theories. I will illustrate these cases with examples from the study of human decision-making.