Machine learning model evaluation in AI focuses on assessing the performance and accuracy of predictive models. This evaluation process typically involves using metrics such as accuracy, precision, recall, and F1 score to determine how well a model generalizes to unseen data. Through techniques like cross-validation and confusion matrices, data scientists can identify areas for improvement, fine-tune algorithms, and select the best model for deployment. This process is essential to ensuring that AI systems provide reliable and effective outputs in real-world applications.