Measuring outcomes in pediatric cardiac care has been one of the more widespread, and at the same time controversial and often polarizing, quality improvement initiatives undertaken in the medical field. Risk models, such as the Society of Thoracic Surgeons Congenital Heart Surgery Risk Model, have been developed to account for comorbidities while predicting the expected mortality for a given surgical encounter. In this issue of the journal, Bertsimas and colleagues report on machine learning approaches to predict adverse outcomes in congenital heart surgery using the European Congenital Heart Surgeons Association's congenital database. A head-to-head comparison of machine learning models and the currently available risk models utilizing the same data set are required to better understand the strengths and weaknesses of each of these approaches. Such a focused analysis will shed light on future approaches for risk modeling, which will undoubtedly continue to benefit from the guidance provided by expert clinical intuition.