Singular Value Decomposition (SVD) is a mathematical technique used in linear algebra to factorize a matrix into three simpler matrices. It breaks down a given matrix into a product of three components: U, a matrix of left singular vectors; Σ, a diagonal matrix of singular values; and V^T, a matrix of right singular vectors. This decomposition helps in understanding the structure of the original matrix. SVD is widely used in various applications, including data compression, image processing, and recommendation systems. By reducing the dimensionality of data, SVD can reveal important patterns and relationships, making it easier to analyze and interpret complex datasets.