Space complexity measures the amount of extra memory an algorithm uses as a function of the input size, not counting the input itself. It is expressed in Big O notation and is often traded off against time complexity: you can save time by storing more results.
Example
Reversing a list by creating a copy uses O(n) memory; reversing it in place uses O(1).