"O(n^2)" is a notation used in computer science to describe the time complexity of an algorithm. It indicates that the time it takes to complete the algorithm grows quadratically as the size of the input data increases. For example, if the input size doubles, the time taken can increase by four times. This complexity often arises in algorithms that involve nested loops, where each loop iterates over the entire dataset. A common example is the bubble sort algorithm, which compares each element with every other element, leading to a performance that can be inefficient for large datasets.