What does Big-O Notation describe in algorithms?

Big-O Notation is a mathematical notation used to describe the performance of an algorithm in terms of its time complexity and space complexity, particularly as the size of the input data increases. It provides a high-level understanding of how the runtime or memory usage of an algorithm grows relative to the input size, allowing developers to compare the efficiency of different algorithms without getting bogged down in specific implementation details.

For instance, if an algorithm has a time complexity of O(n), it means that the time it takes to complete the algorithm grows linearly with the size of the input data. This is crucial for developers when deciding which algorithm to use in a given situation, especially as the scale of the data can have a significant impact on performance and resource consumption.

In contrast, other choices do not accurately describe Big-O Notation; they may refer to related concepts but do not capture its specific focus on performance metrics.