Penalized least squares is a statistical method that modifies the least squares approach by adding a penalty term to the objective function, which helps in controlling the complexity of the model and prevents overfitting. This technique is particularly useful in high-dimensional data settings where the number of predictors exceeds the number of observations, enabling the selection of simpler models that generalize better to new data.