What does big O notation describe?

Big O notation is a mathematical representation used to describe the upper limit or worst-case scenario of an algorithm's time or space complexity. It characterizes how the runtime or memory requirements of an algorithm grow relative to the input size, providing a high-level understanding of its efficiency. By using Big O notation, we can analyze and compare the performance of different algorithms irrespective of their exact implementations or the specifics of their environments.

For example, if an algorithm has a time complexity of O(n^2), it means that in the worst case, the execution time increases quadratically as the input size (n) increases. This helps developers understand how scalable an algorithm is when dealing with large datasets.

Thus, Big O notation is essential for evaluating algorithms in terms of their efficiency and scalability, which is why the choice indicating that it describes the upper limit of an algorithm's time or space complexity is the correct understanding of its purpose.