Singular Value Decomposition (SVD) is a mathematical technique used in linear algebra to factorize a matrix into three other matrices, revealing the intrinsic geometric structure of the data. It is widely used in areas such as signal processing, statistics, and machine learning for dimensionality reduction and noise reduction, among other applications.