Big O notation is a mathematical concept used in computer science to describe the efficiency of algorithms. It provides a way to express the upper limit of an algorithm's running time or space requirements as the input size grows. This helps developers understand how an algorithm will perform under different conditions, especially with large datasets. The notation uses symbols like O(n), O(log n), and O(n^2) to represent different growth rates. For example, O(n) indicates that the time or space grows linearly with the input size, while O(n^2) suggests a quadratic growth, which can become inefficient as the input increases.