  We develop a unifying framework for interpolatory $\mathcal{L}_2$-optimal
reduced-order modeling for a wide classes of problems ranging from stationary
models to parametric dynamical systems. We first show that the framework
naturally covers the well-known interpolatory necessary conditions for
$\mathcal{H}_2$-optimal model order reduction and leads to the interpolatory
conditions for $\mathcal{H}_2 \otimes \mathcal{L}_2$-optimal model order
reduction of multi-input/multi-output parametric dynamical systems. Moreover,
we derive novel interpolatory optimality conditions for rational discrete
least-squares minimization and for $\mathcal{L}_2$-optimal model order
reduction of a class of parametric stationary models. We show that bitangential
Hermite interpolation appears as the main tool for optimality across different
domains. The theoretical results are illustrated on two numerical examples.
