  Machine learning models, while progressively advanced, rely heavily on the
IID assumption, which is often unfulfilled in practice due to inevitable
distribution shifts. This renders them susceptible and untrustworthy for
deployment in risk-sensitive applications. Such a significant problem has
consequently spawned various branches of works dedicated to developing
algorithms capable of Out-of-Distribution (OOD) generalization. Despite these
efforts, much less attention has been paid to the evaluation of OOD
generalization, which is also a complex and fundamental problem. Its goal is
not only to assess whether a model's OOD generalization capability is strong or
not, but also to evaluate where a model generalizes well or poorly. This
entails characterizing the types of distribution shifts that a model can
effectively address, and identifying the safe and risky input regions given a
model. This paper serves as the first effort to conduct a comprehensive review
of OOD evaluation. We categorize existing research into three paradigms: OOD
performance testing, OOD performance prediction, and OOD intrinsic property
characterization, according to the availability of test data. Additionally, we
briefly discuss OOD evaluation in the context of pretrained models. In closing,
we propose several promising directions for future research in OOD evaluation.
