Binary Classification is a type of supervised learning where a model predicts one of two possible classes (e.g., spam vs. not spam, fraud vs. legitimate). Common algorithms include logistic regression, decision trees, support vector machines, and neural networks. Evaluation metrics such as accuracy, precision, recall, F1-score, and AUC-ROC are used to assess model performance.