Benign overfitting is a type of overfitting that occurs when the model performs well on the training data, but does not generalize to unseen data. It is usually caused by an overly complex model that has too many parameters and does not account for noise in the data. To address benign overfitting, researchers have proposed various techniques such as regularization, feature selection, and cross-validation. These techniques can help reduce the complexity of the model and prevent it from memorizing noise in the training data.