XGBoost is a powerful boosting algorithm, but it can overfit if not controlled. Regularization helps by simplifying trees, pruning unnecessary splits, and balancing bias–variance. This guide explains overfitting, how XGBoost improves on Gradient Boosting, and key parameters like gamma, lambda, max_depth, min_child_weight, learning rate, subsample, and early stopping to build robust models.