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The first element pivot strategy in quicksort involves selecting the very first element of the array as the pivot. This method is very simple to implement since no additional calculations or comparisons are needed. Just take the first item and proceed with the sorting algorithm.

However, the main drawback of using the first element pivot is that its performance can be quite poor in certain situations. For instance, if your array is already sorted or nearly sorted, always selecting the first element means you'll often end up with highly unbalanced partitions, which leads to increased recursion depth and a higher number of comparisons and swaps.

In summary, while easy to apply, the first element pivot strategy is best avoided for sorted or nearly sorted datasets.

The last element pivot strategy in quicksort is another simple method where you select the very last element of the array as the pivot. Like the first element pivot, this technique is straightforward and doesn't require much computation.

Yet, similar to the first element pivot, using the last element as the pivot can also result in poor performance when dealing with sorted or nearly sorted arrays. In such cases, the pivot will either be the highest or the lowest value, leading to unbalanced partitions, longer recursion chains, and inadequate efficiency.

Overall, the last element pivot is easy to implement but potentially inefficient for pre-sorted datasets.

The median-of-three pivot strategy in quicksort is a more sophisticated and effective approach. Here, you select the pivot by considering the median value among three elements: the first element, the middle element, and the last element of the array.

This method aims to approximate the true median of the array, which helps in creating more balanced partitions. By better balancing the partitions, it minimizes the likelihood of encountering the worst-case performance scenario associated with sorted or nearly sorted arrays.

To implement this strategy, you'll need a few more comparisons and possibly a swap to place the median value as the pivot. The slight increase in complexity and computation is usually outweighed by the overall performance improvement.

In essence, the median-of-three pivot is a powerful technique that delivers more consistent and efficient performance across various kinds of input data.