SVD, or Singular Value Decomposition, is a mathematical technique used in linear algebra to factorize a matrix into three simpler matrices. This decomposition helps in understanding the structure of the original matrix, making it easier to analyze and manipulate data. SVD is particularly useful in applications like image compression and recommendation systems. In SVD, a matrix is broken down into U, Σ, and V matrices. Here, U contains the left singular vectors, Σ is a diagonal matrix with singular values, and V contains the right singular vectors. This method allows for dimensionality reduction, enhancing computational efficiency while preserving essential information.