Overcomplete dictionaries are used in signal processing and machine learning to represent data with more basis vectors than the dimensionality of the space, enabling sparse and efficient representations. This redundancy allows for greater flexibility in capturing complex structures and patterns in data, often leading to improved performance in tasks like denoising, compression, and feature extraction.