Space complexity refers to the amount of memory an algorithm uses in relation to the size of the input data. It measures both the fixed and variable space required during the execution of the algorithm. Fixed space is the memory needed for constants and variables, while variable space depends on the input size, such as arrays or linked lists. 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 memory usage grows as the input size increases.