What does 'big O' notation describe in computer science?

'Big O' notation is a mathematical concept used in computer science to describe the performance characteristics of algorithms, particularly their efficiency in relation to the size of the input data. It provides an upper bound on the time complexity or space complexity of an algorithm, allowing developers and computer scientists to estimate how the execution time or memory usage grows as the input size increases.

When analyzing algorithms, 'big O' notation helps identify the worst-case scenario regarding the time it takes to execute or the space required to complete the task. For instance, an algorithm that runs in linear time is denoted as O(n), meaning if the size of the input increases, the time taken will increase proportionally. This notation is crucial for understanding scalability and making informed decisions about which algorithms to use based on the size and characteristics of the input data.

In contrast to the other choices, which focus on aspects unrelated to algorithm efficiency, 'big O' notation specifically addresses how algorithms perform with varying input sizes, highlighting its importance in algorithm design and analysis.