Additive models are statistical models where the effect of each predictor is added together to predict the response variable, making them flexible for capturing non-linear relationships without assuming interactions between predictors. They are particularly useful in scenarios where the relationship between predictors and the response is complex but can be decomposed into simpler, additive components.