Two brain activity recording paradigms in humans have emerged as increasingly more popular tools for studying brain function in health and in disease, namely resting-state and naturalistic stimulation. These two techniques attempt to capture brain activity 'in the wild' when it is unconstrained by any specific task and thus reflect more naturalistic modes of operation of the brain. The complexity, very high-dimensional nature, a suite of potential applications and lack of standard, straightforward analysis tools make machine learning very attractive for this kind of data. In this thesis, we draw upon recent advances in machine learning, fueled by the success of deep learning, to develop models that can capture the full richness of this data. Resting-state fMRI (rs-fMRI) has enormous potential to advance our understanding of the brain's functional organization and how it is altered by damage or disease. Over the last decade, substantial effort has been devoted to using rs-fMRI for classification of a wide range of neuropsychiatric conditions, such as Alzheimer's disease, schizophrenia, autism spectrum disorder etc. While a growing number of studies have demonstrated the promise of machine learning algorithms for rs-fMRI based clinical or behavioral prediction, most prior models have been limited in their capacity to exploit the richness of the data. The first part of this thesis describes our work on developing novel machine learning approaches for deriving subject level predictions from rs-fMRI scans. We propose a novel volumetric Convolutional Neural Network framework that takes advantage of the full-resolution 3D spatial structure of rs-fMRI data and fits non-linear predictive models. We showcase our approach on a challenging large-scale dataset and report state-of-the-art accuracy results on rs-fMRI-based discrimination of autism patients and healthy controls. The second part of this thesis is aimed at developing predictive models that can capture information processing within the brain under naturalistic stimulation more stringently than existing approaches. Brain activity recordings of healthy subjects during "free viewing" of movies present a powerful opportunity to build ecologically sound and generalizable models of sensory systems, also known as encoding models. Deep neural networks trained on image or sound recognition tasks have emerged as powerful models of computations underlying sensory processing in the brain, surpassing traditional models of image or sound representation based on Gabor filters and spectro-temporal filters, respectively. While this success is promising, existing encoding models based on deep neural networks have been limited in their focus on limited portions of the sensory space under naturalistic stimulation, ignoring the complex and dynamic interactions of modalities (audio and vision) in this inherently context-rich paradigm. In the second part of this thesis, we will introduce our research with predictive models of cortical responses that aims to capture several critical inductive biases about information processing in the brain : namely, hierarchical processing, assimilation over longer timescales, attentional modulation and multi-sensory auditory-visual interactions. We will describe our efforts in capturing these phenomena in models of the brain and will share our latest findings from this novel computational approach. Finally, we describe our ongoing efforts to characterize neural response properties in the visual cortex under 'ecological' conditions systematically in an entirely data-driven fashion using computational models. Together, our findings illustrate how computational models overcome the tradition of excessive reductionism in cognitive neuroimaging by providing a general-purpose framework that abstracts away from the particulars of the experimental approach and can be used to describe multiple experiments at the same time.