The Receiver Operating Characteristic (ROC) curve is a tool commonly used by data scientists to understand the performance of classifiers. It shows the true positive rate vs. the false positive rate for a range of propensity thresholds going from 0 to 1 (remember, your propensity score will range between 0 and 1). It is often used to compare models in an apples-to-apples way since it does not depend on your choice of propensity threshold, whereas many other error metrics do.