As a utility to quantify the "difficulty" of the validation strategy, mofdscribe also implements a helper method that performs adversarial validation. Adversarial validation is a technique that has been popularized in data science competitions as a way to measure-with only one numberthe difference between training and test distribution but also to identify the most relevant features for a potential difference. For this, one simply trains a classifier to distinguish training from test examples. If the area under the receiver-operating curve (ROC-AUC) is close to 0.5, the classifier fails to distinguish the two datasets. However, if it does not (i.e. ROC-AUC close to 1), analyzing the feature importance can reveal the most relevant features contributing to the difference (which one might decide to remove to improve generalization). An application of this concept is shown in Case Study 2.