Measuring machine learning performance involves selecting appropriate evaluation metrics, splitting the data into training and testing sets, and using techniques like cross-validation for robust assessment. Common metrics include accuracy, precision, recall, F1-score, and AUC-ROC. The choice of metrics depends on the problem type and specific requirements, such as prioritizing false positives over false negatives. To ensure model generalization, use techniques like k-fold cross-validation, which trains and validates the model on different data subsets multiple times, providing an averaged performance measure.