Big O notation describes algorithm performance as data size increases, focusing on the number of steps required. It categorizes complexities such as constant time (O(1)), linear time (O(n)), and quadratic time (O(n²)). Understanding these complexities helps evaluate algorithm efficiency, especially with larger datasets.