Performance metrics are used to evaluate machine learning algorithms and models. Key methods include confusion matrix, accuracy, precision, recall, specificity, and F1 score. The confusion matrix is a table that allows visualization of model performance, while accuracy measures correct predictions over total predictions. Precision focuses on avoiding false positives and recall focuses on avoiding false negatives. The F1 score calculates the harmonic mean of precision and recall to provide a single combined metric. These metrics help select the best performing algorithm and optimize model performance.