Generalized Additive Models (GAMs) are a flexible extension of generalized linear models that allow for non-linear relationships between the dependent and independent variables by using smooth functions. They are particularly useful for capturing complex patterns in data without assuming a specific parametric form, making them a powerful tool for regression analysis and prediction tasks.