When you start reading measure theory, the first motivation presented is
we want to find some nice sets we can measure.
The solution is $\sigma$-algebras.
Then we define a random variable as $X: \Omega \rightarrow \mathbb{R}$. Both $\Omega$ and $\mathbb{R}$ are equipped with $\sigma$-algebras.
However, the thing we want to measure is $X \in A$, for $A \in \mathbb{B}$, i.e. we want $P(X^{-1}(A))$. But $X^{-1}(A) \in \mathcal{F}$, the $\sigma$-algebra in $\Omega$. So what we want to measure is the sets in $\Omega$.
So, the only purpose of $\mathbb{B}$ seems to be to ensure that when we take $X^{-1}$ on a Borel set, we get something that lies in $\mathcal{F}$ (since $X$ is measurable).
But ... that can be accomplished by any other $\sigma$-algebra as well. Let "The Bublu Sets" be any other $\sigma$-algebra on $X$ which contains enough sets of interest. Then let us define a random variable as a map from $\Omega$to $\mathbb{R}$ which is measurable with respect to The Bublu Sets. Then we can ask questions like $P(X \in A)$ where $A$ is a Bublu Set. This theory seems identical to the one with the Borel sets?
So, what is so special about the Borel sets? Why have I heard this name "Borel" so much in probability and measure theory, when all it seems to do is accomplish something that any other $\sigma$-algebra would also do, such as The Bublu Sets.