This paper addresses recent developments in model-reduction techniques applicable to fluid flows The main goal is to obtain low-order models tractable enough to be used for analysis and design of feedback laws for flow control, while retaining the essential physics. We first give a brief overview of several model reduction techniques. including Proper Orthogonal Decomposition [3], balanced truncation [8, 9], and the related Eigensystem Realization Algorithm [5, 6], and discuss strengths and weaknesses of each approach We then describe a new method for analyzing nonlinear flows based on spectral analysis of the Koopman operator a linear operator defined for any nonlinear dynamical system We show that, for an example of a Jet in crossflow, the resulting Koopman modes decouple the dynamics at different timescales more effectively than POD modes, and capture the relevant frequencies more accurately than lineal stability analysis