Cross-Domain Generalization refers to the ability of a model trained on one domain to perform well on a different, but related, domain without explicit retraining. It is a critical challenge in machine learning, as it involves adapting learned knowledge to new, unseen environments, thereby enhancing the model's robustness and applicability.