Tensor Factorization is a mathematical technique used to decompose multi-dimensional arrays, known as tensors, into simpler, lower-dimensional components. This process helps in uncovering hidden patterns and relationships within complex data, making it easier to analyze and interpret. In various fields like machine learning, computer vision, and recommendation systems, tensor factorization is employed to enhance data representation. By breaking down tensors, it allows for more efficient storage and processing, ultimately improving the performance of algorithms that rely on large datasets.