Logistic regression is a classification algorithm that estimates the probability of a binary outcome using the sigmoid function: P(Y=1|X) = 1 / (1 + e^(-βX)). The model is trained using maximum likelihood estimation (MLE) and evaluated using accuracy, precision-recall, and ROC-AUC.