Big O notation is a mathematical concept used in computer science to describe the efficiency of algorithms. It provides a way to express how the runtime or space requirements of an algorithm grow as the size of the input increases. For example, an algorithm with a time complexity of O(n) means that if the input size doubles, the time it takes to complete the algorithm will also roughly double. Understanding Big O notation helps developers compare different algorithms and choose the most efficient one for their needs. Common complexities include O(1) for constant time, O(n) for linear time, and O(n^2) for quadratic time, each indicating how performance scales with larger inputs.