ically minimized during model training and hyperparameter tuning to avoid over/underfitting. However, the outof-distribution (OOD) generalization error constitutes a more useful performance metric for assessing a model's true ability to generalize to unseen data. This error originates from either lack of knowledge (e.g., imbalance in data, or poor data representation) or sub-optimal model architecture and is referred to as being epistemic. Evaluating OOD generalization, however, requires more careful considerations during data splitting.