What is the purpose of Big O notation in algorithm analysis?

Big O notation is used to estimate the efficiency of algorithms, showing how execution time or memory usage grows depending on the size of the input data. It helps to understand how well an algorithm scales and to compare it with others.

For example, if an algorithm has a complexity of O(n), it means that the execution time grows linearly with the increase in the number of elements. An algorithm with O(n²) will run slower on large data because the time increases quadratically.

Using Big O allows for choosing optimal algorithms for tasks, especially when performance and resources are important.