Space complexity refers to the amount of memory space required by an algorithm to execute as a function of the size of the input data. It includes both the space needed for the input values and the additional space required for variables, data structures, and function calls during the algorithm's execution. Understanding space complexity helps in evaluating the efficiency of algorithms, especially when dealing with large datasets. It is often expressed using Big O notation, which provides a high-level understanding of how the memory requirements grow as the input size increases, allowing developers to make informed choices about algorithm selection and optimization.