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When we talk about algorithm overhead, we are referring to the extra resources that an algorithm consumes beyond the basic functionality needed to solve the given problem. These resources can include additional memory space, computational power, and time.

In recursive algorithms, the overhead is particularly noticeable because each recursive call adds extra work for the system. This work includes saving the current state, managing the call stack, and creating new execution contexts (stack frames) for each call. These activities require more memory and processor time, affecting the algorithm's overall efficiency.

Iterative algorithms, by contrast, use loops to repeat operations, thus avoiding the creation of multiple execution contexts and typically resulting in lower overhead. They work within a single execution context that updates as the loop progresses, which requires less memory and generally executes faster.

To help students better comprehend, it's useful to visualize algorithm execution. You can imagine a recursive algorithm as a tree that grows with every function call, with each branch representing a new call, while an iterative algorithm is akin to a single path being traversed with different values.

Computational complexity is a measure of the efficiency of an algorithm, often expressed in terms of the amount of time or number of steps it takes to complete a given task as the size of the input changes. It is categorized into two types: time complexity and space complexity.

For recursive algorithms, the time complexity can rapidly increase as the number of recursive calls grows, especially for algorithms without optimizations like memoization. Iterative algorithms typically showcase more predictable time complexities, as each step in an iteration is generally consistent in the time it takes.

Understanding computational complexity allows students to predict the performance of an algorithm and choose the most efficient one based on the problem's constraints. For example, an iterative algorithm may be preferred for large datasets where recursive approaches would lead to significant performance penalties.

The concept of memory allocation in algorithms is crucial for understanding the differences in how recursive and iterative algorithms use system resources. Memory allocation refers to the process of assigning memory space to various parts of a program during its execution.

In recursive algorithms, memory allocation becomes a more complex issue because each recursive call requires its own space in the call stack. This means that a high number of calls can lead to a large stack, and potentially, a stack overflow error.

In contrast, iterative algorithms tend to use a consistent amount of memory throughout their execution, which makes them more memory efficient. Iterative algorithms allocate memory for the variables used in the loop once, and update these variables with each iteration, instead of allocating new memory on each step.

Stack frame management is an important aspect of any recursive algorithm's operation. A stack frame, also known as an activation record, is a data structure that stores information about a function's execution state, including variable values and the location in the program to return control after the function completes.

Every time a recursive function is called, a new stack frame is created and pushed onto the call stack. Once the function completes, its frame is popped off the stack. This push and pop process is a critical part of the overhead associated with recursion.

Effective stack frame management ensures that the recursive calls complete without running out of memory. It's important for students to grasp that recursion depth directly impacts the number and size of stack frames, and thus they need to consider the limitations of stack memory when writing recursive algorithms. For iterative solutions, stack frame management is greatly simplified as there is typically a single frame used throughout the execution of the loop.