Compressed Sparse Row (CSR) is a storage format used for sparse matrices, optimizing memory usage by only storing non-zero elements along with their positions. It efficiently supports matrix operations like multiplication and transposition, making it ideal for applications in scientific computing and machine learning where large, sparse datasets are common.