For a convex class of functions $F$, a regularization functions $Ψ(\cdot)$ and given the random data $(X_i, Y_i)_{i=1}^N$, we study estimation properties of regularization procedures of the form \begin{equation*} \hat f \in {\rm argmin}_{f\in F}\Big(\frac{1}{N}\sum_{i=1}^N\big(Y_i-f(X_i)\big)^2+λΨ(f)\Big) \end{equation*} for some well chosen regularization parameter $λ$. We obtain bounds on the $L_2$ estimation error rate that depend on the complexity of the "true model" $F^*:=\{f\in F: Ψ(f)\leqΨ(f^*)\}$, where $f^*\in {\rm argmin}_{f\in F}\mathbb{E}(Y-f(X))^2$ and the $(X_i,Y_i)$'s are independent and distributed as $(X,Y)$. Our estimate holds under weak stochastic assumptions -- one of which being a small-ball condition satisfied by $F$ -- and for rather flexible choices of regularization functions $Ψ(\cdot)$. Moreover, the result holds in the learning theory framework: we do not assume any a-priori connection between the output $Y$ and the input $X$. As a proof of concept, we apply our general estimation bound to various choices of $Ψ$, for example, the $\ell_p$ and $S_p$-norms (for $p\geq1$), weak-$\ell_p$, atomic norms, max-norm and SLOPE. In many cases, the estimation rate almost coincides with the minimax rate in the class $F^*$.