Sparse modeling is a technique in machine learning and statistics that focuses on representing data with a minimal number of non-zero parameters, thereby enhancing interpretability and efficiency. It is particularly useful in high-dimensional data settings where it helps in feature selection and reducing overfitting by imposing sparsity constraints on the model parameters.