Big-O Notation, pronounced as "Big Oh," is a standard way to describe how the running time or memory usage of an algorithm scales with the size of the input, n. It focuses on the upper bound of growth, which helps us understand the worst-case scenario of how an algorithm performs as the input gets larger.

• Purpose: It measures the efficiency of algorithms by providing a way to compare their performance for large inputs.