Final answer:
The best-case scenario in terms of complexity is represented by 'c) Worst O(n)', indicating a linear time complexity, which is more efficient than 'O(nlogn)'.
Step-by-step explanation:
In terms of complexity, when considering the best-case scenario, the correct notation is c) Worst O(n). This notation indicates that in the best-case scenario, the algorithm runs in linear time complexity, which grows directly in proportion to the size of the input data (n). Comparatively, O(nlogn), whether average or best case, represents a more complex scenario where the run time increases at a rate higher than linear due to the multiplicative log factor.
Time complexity is an essential concept in computer science that describes how the number of steps required by an algorithm increases as the input size increases. Best-case scenario refers to the minimum time an algorithm will take, and for most algorithms, this would be less than or equal to the complexity in the average or worst cases.