Sparse representations are a way of encoding information such that only a small number of components are non-zero, which can lead to more efficient data processing and storage. They are particularly useful in machine learning and signal processing for tasks like feature selection, compression, and noise reduction, leveraging the idea that many real-world data sets are inherently sparse in nature.