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 in comparing the performance of different algorithms, especially for large datasets. In Big O notation, we focus on the most significant factors that affect performance, ignoring constants and lower-order terms. Common complexities include O(1) for constant time, O(n) for linear time, and O(n^2) for quadratic time. Understanding these complexities helps developers choose the most efficient algorithms for their applications.