Explain best case, average case, and worst case complexity.

Best case is the minimum time/space needed (e.g., binary search finding target at middle - O(1)). Average case represents expected performance over all possible inputs (binary search - O(log n)). Worst case is maximum resources needed (binary search - O(log n)). Big O typically describes worst case, while Big Theta describes tight bounds. Understanding all cases helps choose algorithms for specific use cases and input distributions.