Does it make sense to talk about minimization of an objective function over function spaces?

I recent years, particularly in CS and machine learning, I notice that many authors write things like (see equation (4) for one of the thousands of examples)

Let $$f$$ be a function/hypothesis/policy in some function space $$\mathcal{F}$$, then the goal of this "learning problem" is to find $$f^\star$$ such that $$f^\star = \text{argmin}_{f\in \mathcal{F}} \; \; L(f)$$, where $$L$$ is some loss function

Is this a good practice to write an optimization problem this way?

Does it even make sense? For example, I understand what a minimum point means with respect to parameters, i.e., $$1 < 2$$. But I don't understand what minimum means with respect to functions, as the space of functions is not linearly ordered, i.e., does not make sense to write $$\sin < \cos$$.

Wouldn't it be clearer, if not more correct, to write it as a minimization problem over the space of the parameters associated with the function instead?

All the algorithms for solving the optimization problem involves the parameters associated with a function as opposed to constructing the function directly.

For instance, the gradient descent is written as,

$$w_{k+1} = w_k - \eta \nabla L(w_k), w_k \in \mathbb{R}^n$$

and not

$$f_{k+1} = f_{k} - \eta \nabla f_k, f_k \in \mathcal{F}$$

Can anyone chime in whether writing a minimization problem in terms of the function is a good practice?

The $$L$$ in this context is a map from the function space $$\mathcal{F}$$ to the real numbers $$\Bbb{R}$$; symbolically $$L: \mathcal{F} \to \Bbb{R}$$. You are right that the function space $$\mathcal{F}$$ has no ordering defined on it, but the real numbers do; and this is the ordering we are making use of! What we are doing is for each function $$f$$ in the function space $$\mathcal{F}$$, we consider the real number $$L(f)$$, and then we ask if there is a specific function $$f^*\in \mathcal{F}$$ which minimizes $$L$$.

Said more concisely, two questions have to be answered: whether there exists an $$f^* \in \mathcal{F}$$, such that for all $$f \in \mathcal{F},$$ it is true that $$L(f^*)\leq L(f)$$. Secondly, if such an $$f^*$$ exists, how we can we find it? (from your specific quote, it seems the authors are only interested in the second question)

Wouldn't it be clearer, if not more correct, to write it as a minimization problem over the space of the parameters associated with the function instead?

Yes this is indeed the correct way to pose a minimization problem. The confusion you're having is you're thinking of $$f$$ as the function, and something else (possibly elements of the domain of $$f$$) as the parameters. But in this context, $$L: \mathcal{F} \to \Bbb{R}$$ is the function we wish to minimize, and $$f \in \mathcal{F}$$ are the "parameters" (for $$L$$).

As mentioned in the comments, the subject of Calculus of Variations is full of such problems, one of the simplest to state is the following:

Given two points $$p,q$$ in the plane ($$\Bbb{R}^2$$), find the curve $$\gamma$$ with shortest length joining the two points. Here, we have a certain function space $$\mathcal{F}$$ as our "parameters" (those whose initial and ending points are $$p$$ and $$q$$ respectively), and we have a map $$L: \mathcal{F} \to \mathbb{R}$$, which to each curve $$\gamma$$, assigns its length $$L(\gamma)$$. (of course it needs to be formulated more precisely, but the general question should be easy enough to understand)

In your quote, $$L$$ is a functional, which takes a function and returns a real number. It absolutely makes sense to talk about optimizing the function argument $$f$$ for given operators like $$L$$, and the field of Functional Analysis is dedicated to studying these sorts of optimization problems, among other things.